The pharmaceutical industry spends more than $40 billion worldwide on research and development of new drugs, but only 5 to 10% of drugs entering the clinical phase of drug development will be approved for marketing. Currently the average cost per successful drug development program is between $500 and $900 million, and its duration is on average 8 to 12 years (1,2,3). This average cost figure is this high because 75% of that $500–900 million the pharmaceutical companies spend per drug is related to drug failures along the way (2).
An important reason for the high failure rate in clinical trials is the poor predictive value of currently used screening technologies for biological validation, pharmacological testing, and screening for success or failure of chemical entities and biologicals in clinical trials involving human subjects. These screening technologies are based on in vitro cell-based screening models and in vivo animal models, which often lack or inadequately represent the clinical disease phenotype of the patients in which the tested chemical entities or biologicals are intended to be used in the future. Therefore, success of these chemical entities or biologicals in these models does not necessarily translate into clinical success in patients. Hence, the majority of chemical entities or biologicals, while successful in these preceding screening and animal models, fail in clinical trials, particularly in late phase II and phase III trials (38). It has been estimated that more than 90% of new chemical entities (NCEs) fail in clinical trials, of which approximately two third fail for pharmacodynamic reasons (lack of efficacy and/or an unacceptable adverse event profile); the remaining third fail for pharmacokinetic reasons (3).
According to a Lehman Brothers report, the problem of poorly predictive models will become increasingly worse in the genomic era because a higher number of inadequately biologically validated NCEs will enter clinical trials (1). This will decrease the overall success rate of clinical trials even further. This report predicts that the average R&D budget needed to develop an NCE will have to increase from a current value of $500–900 million to $1.5 billion in the next five years, unless significant improvements are made.
The lack of available predictive technologies for success or failure in clinical trials leads to the current situation. Long and expensive preceding studies (in general more than five years and upfront investments of tens to hundreds of million dollars) are needed both in animals and humans before success or failure of NCEs can be established in phase II or phase III studies. Until better models are developed, the majority of NCEs will fail in phase II and III trials, either due to lack of efficacy or an unfavorable side effect profile. A cell-based method that could better predict success or failure in phase II and III trials, without the need for large up front investments, would represent a tremendous advantage from a pharmaco-economic perspective, as it would eliminate drug candidates or biologicals likely to fail early on, without the requirement of large upfront investments. Eventually, such a method would allow the production of medicines that are safer and more effective, at a much-reduced cost. In addition, such a model would reduce the need for in vivo animal testing.
Furthermore, most drugs show significant inter-individual variation in therapeutic efficacy and adverse event outcome (4,5,6,7,8). Evaluation of effectiveness and adverse event profile is still based on the average response of a study group. Inspection of the data from individual subjects, however, usually reveals significant numbers of patients with little or no response, as well as those who have dramatic responses. In cases of complex diseases, this ‘one-drug-fits-all’ attitude subjects patients to empirical trial-and-error periods before acceptable treatment regiments are found (4,8).
Assays for the personalized medicine application and the identification of responders/non-responders in clinical trials are currently based on single nucleotide polymorphisms (SNPs) or haplotypes (4,8). Despite major investments made to develop the SNP approach for these applications, the numbers of successfully developed assays are small and their predictive value is often only modest. The trial-and-error nature of current clinical practice is a significant economic burden on the health care system and keeps many patients effectively untreated for sustained periods of time. A test tool that could predict whether a registered medication would be effective in a specific patient in a timely manner would offer tremendous benefit for patients and healthcare economics.
Moreover, the same principle could also be used to identify responders/non-responders in clinical trials with not yet registered NCEs. A large number of patients has to be recruited for each individual clinical phase II and III trials, in order to demonstrate efficacy and safety in a statistically meaningful manner. Typically 50 to 200 patients are recruited in phase II and hundreds to thousands of patients in phase III. An important reason for the large numbers of patients are needed is the strong inter-individual variation in therapeutic efficacy and adverse event outcome in a randomly recruited patient population. The elimination of non-responders in these clinical trials would reduce the variability in trial outcome. This, in turn, would reduce the need for a large sample size of patients dramatically. Therefore, a test tool that avoids inclusion of patients likely to be non-responders in a clinical trial would lead to cost reduction on the order of hundreds of millions up to billions of dollars.
The development of predictive cell-based models has been hampered for various reasons, including the availability of human cells and tissues, in particular with the right genotype and disease phenotype, and the identification of validated cellular endpoints that have proven to predict in vivo responses after drug exposure. An ideal cell-based model should be using target cells or target tissues from patients who would be ultimately treated with the tested dugs. The availability of human cells for drug testing is limited, and often from questionable quality due to limitations in the preservation and the homogeneity of excised human tissues. Embryonic stem cell-based technologies are currently considered, but have inherent restrictions due to ethical considerations, and limitations in defining disease phenotype in these embryos that do not have manifestations of disease to be treated by investigational drugs or biologicals. Therefore the value of embryonic stem cells to predict pharmaco-responses in specific patient populations with a well-defined disease phenotype is restricted. The identification of cellular pharmaco-response that reliably predicts pharmaco-responses in real patients with defined disease phenotype is another important obstacle. Ideally, this would require an experimental setting in which both cellular endpoints and in vivo patient endpoints after exposure to the same drug can be obtained to allow for a within-subject comparison, and to establish a strong in vitro/in vivo correlation.
Accordingly, a need remains in the art for a cell-based assay that can better predict success or failure of NCEs in phase II and III trials. A need also remains in the art for an assay that can identify patients likely to be non-responders in a clinical trial. Finally, a need remains in the art for an assay that can predict whether a medication or a chemical entity will be effective in a specific patient.